Samsung Unveils LPDDR5X-PIM: Compute-in-Memory That Triples AI Inference

By Carlos Montiel | Enterprise AI Specialist
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Published: 2026-08-28 | By: Carlos Montiel | Reading time: ~4 minutes

Samsung Electronics unveiled its LPDDR5X-PIM chip at Hot Chips 2026, held at Stanford: low-power memory with compute logic built directly into the DRAM, which in tests with Llama 3.1 nearly tripled inference performance versus conventional LPDDR5X memory, using the same physical packaging.

What "Processing-in-Memory" Is and Why It Matters

PIM (Processing-in-Memory) is a design that runs certain compute operations directly inside the memory chip itself, instead of constantly moving data between memory and the processor or AI accelerator. That data movement — not the compute itself — is one of the biggest energy and speed bottlenecks in AI workloads. According to Samsung, LPDDR5X-PIM delivers 614 GB per second of bandwidth for PIM operations, eight times more than conventional LPDDR5X's 76.8 GB per second.

Results with Real Models and Compatibility

In tests reported by Samsung and confirmed by trade outlets like Tom's Hardware and ServeTheHome, running Llama 3.1 on LPDDR5X-PIM raised throughput from 27 to 81.3 tokens per second — roughly a 3x improvement — and cut task completion time from 12.3 to 5.4 seconds. The key competitive edge is that the chip uses the same 561-ball packaging as standard LPDDR5X, allowing it to directly replace it in existing systems without redesigning the board or device.

Samsung LPDDR5X-PIM — key specs (Hot Chips 2026) Capacity: 16 GB PIM bandwidth: 614 GB/s (8x vs. 76.8 GB/s for standard LPDDR5X) Llama 3.1 improvement: 27 → 81.3 tokens/second (~3x) Task time: 12.3s → 5.4s Compatibility: same 561-ball packaging, drop-in replacement
What this means for companies planning AI infrastructure: the cost and scarcity of high-bandwidth memory (HBM) is one of the biggest factors driving up AI hardware costs in 2026. Memory compatible with standard packaging that triples inference performance with no system redesign is, in the medium term, a signal that running models locally — on mobile devices, PCs, or edge servers — could become cheaper and more accessible. For companies in Latin America evaluating when to move inference workloads outside the cloud for cost or data sovereignty reasons, this class of memory advance is a variable worth watching closely over the next 12 to 18 months, though the direct impact on commercial products will still take time to arrive.
Carlos Montiel
Enterprise AI Solutions Architect
Specialist in LLMs, Agents, and Orchestration
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